
J-LSTM [1]} employs a hybrid CNN-LSTM architecture to capture the discriminative features between untampered and tampered regions.

RGB-N [2]} adopts a two stream parallel network to separately discover tampering features.

ManTraNet [3]} uses a feature extractor to capture the manipulation traces and a local anomaly detection network to localize the manipulated regions.

SPAN [4]} uses a pyramid architecture to and self-attention blocks to model the dependency of image patches.

Transforensics [5]} uses vision transformers with dense self-attention encoders and dense correction modules to model all pairwise interactions between local patches at different scales.

